- Artificial intelligence (AI) has been working itself into health IT infrastructure as organizations need more advanced technology to handle the growing amount of healthcare data.
As AI becomes more of a reality, organizations have to realistically work AI solutions into their IT infrastructure. This can be a challenging process because AI requires a significant amount of computing power and skills to manage the new layers of technology.
Organizations are finding that it’s challenging to integrate AI into their operational processes, according to a recent Tractica report.
AI was created to emulate the human mind and working processes, and can independently solve problems without needing to be programmed to do so. AI can accept new information and learn from it without human intervention.
The computing power behind AI allows it to process information exponentially faster than a human could, fixing problems or drawing conclusions that the human mind would never be able to achieve.
“Enabling AI at the enterprise scale is not a plug-and-play proposition,” Tractica Principal Analyst Keith Kirkpatrick said in a statement. “Significant time, resources, and capital must be deployed, and in most cases, internal company teams are not experienced enough with AI, nor do they have the cutting-edge data science skills to adequately embark upon a truly transformational AI implementation.”
Entities need to decide how they’re going to handle the infrastructure changes needed to process and store data. Organizations must also find the staff needed to manage and monitor the AI solution.
AI is one of the more robust technologies that’s part of the digital transformation, and can be applied to analytics and cybersecurity.
Healthcare entities having a broad surface area is one of the biggest IT infrastructure security challenges facing organizations today. The wider surface area means there are more potentially vulnerable places cyberattackers can take advantage of.
With more ground to cover, IT security staff can be stretched thin and legacy network security systems might not be able to catch evolving security attacks.
Applying AI to cybersecurity solutions will help organizations find gaps in their security infrastructure and prevent future attacks.
AI is also used heavily in healthcare analytics. A computer with AI can look at an image of a healthy brain scan and an image of a brain scan with tumors. The device could then recognize the difference between the two images by breaking them down into machine-readable patterns.
The machine can remember and reference these patterns, then apply them to future images to determine which patterns indicate that a brain tumor is present.
Most healthcare organizations cannot afford to deploy an AI solution on-premises or have the space to accommodate the required hardware.
Cloud-based AI solutions and cloud storage are good options for healthcare organizations.
Cloud-based storage is a flexible storage solution, and often provides healthcare organizations with a more cost-effective storage strategy over traditional on-premise deployments.
When organizations begin to consider the future costs of scaling up based on the increased amount of data, budget concerns come to the forefront of the decision-making process.
On-premise storage solutions require organizations to purchase hardware and only offer a finite amount of space available before additional hardware needs to be added. Cloud services act as a utility with organizations paying monthly or yearly fees based on what they are using.
As organizations need more space, they scale up their cloud service requirements and increase payments accordingly.
AI is still a young technology when it comes to enterprise IT infrastructure implementation, but it is expected grow significantly worldwide over the next several years.
As healthcare organizations look to implement an AI solution in the near future, ensuring the organization’s health IT infrastructure can support it is key to deploying a successful AI analytics solution.